Abstract: Protein-protein interactions have been well known to be one of the most studied complex system. This is because as one of the most fundamental molecules in human bodies, proteins have been linked to various vital functions of the body. Protein aggregation in particular, has been central to a lot of diseases such as Alzheimer and Parkinson disease. Many studies have been done on the aggregation of proteins of the same species. However, our research here focus on understanding the interactions between two peptides that share no similarity both sequentially and structurally. One of the peptides is amylin (IAPP) linked to diabetes, while the other peptide is a prion fragment (PrP106-126) linked to prion disease. Extensive molecular dynamics simulation consisting ~ 22,000 atoms was carried out using enhanced sampling method (Replica exchange molecular dynamics) to simulate the two peptides in solution to elucidate the interaction mechanism between them. Results show that the two peptides form structurally diverse complexes. Hotspots within the sequences of the proteins with high contact probabilities were identified. Extension of the simulation using coarse-grained modelling to simulate large scale oligomers and also the effects of lipids may further provide detailed mechanism of the aggregation. As such, we hope to learn from various other approaches used to model proteins complexity through the conference which will complement our molecular dynamics study, while at the same time providing our own perspective in modelling proteins at the atomistic level and analysing simulation data. My first choice of track is Paper while the second choice of track is Ignite.

Abstract: Ant colonies are self-organised systems. Hence even large-scale colony functions (like foraging or nest construction) must be regulated locally by the workers engaged in it, via interaction with nestmates and the environment. We investigate whether such self-regulation mechanisms exist in one of the most striking collective feats in the ants: the construction of foraging trails in leaf-cutter ant colonies, which can span hundreds of metres. While most ant species rely on pheromone trails to guide their collective movements, Atta leaf-cutter ants build tangible trails cleared down to bare soil of all undergrowth and organic debris, a rare feature among ants. Such trails can greatly increase foraging efficiency despite the costs of construction and maintenance. While recent work investigated the function of such trails, nothing is known about the mechanism of trail construction. In laboratory experiments, we find two concurrent modes of trail clearing behaviour -- ‘one-off’ and ‘repeater’ clearing. By tracking ant movement and obstruction encounters in the field and in laboratory experiments, we identify the regulatory mechanism controlling the extent of trail clearing and the resulting trail dimension. We integrate these concurrent clearing behaviours and the regulatory mechanism in a parameterised stochastic model of the trail clearing dynamics. From this model, we make predictions of the trail clearing behaviour in subsequent experiments with varying foraging conditions, and test them against the empirical results.

Abstract: The human brain is a fascinating and paradigmatic example of a complex system and a natural candidate for network analysis. On one hand, the structural network given by the physical connections between the different brain regions, obtained by Diffusion Tensor Imaging (DTI), has been widely investigated. On the other, many studies have focused on the functional layer, where links represent correlation in the activity of different regions obtained through resting-state functional MRI (rs-fMRI). One of the new challenges in neuroscience is now trying to integrate this different information in order to better understand the interplay between the structure and the function of our brain. In this work we shed light on the mesoscale organization of the multilayer human brains constructed from structural and functional brain information on 21 healthy subjects. In particular, we focus on the following different mesoscopic structures: a) we investigate the presence of motifs, small subgraphs statistically over-represented in the real systems with respect to a suitable null model. In biological networks, the abundance of given subgraphs has been linked to the robustness of the system or to the stability of the dynamical or signalling circuit they represent; (b) we analyse the organization of communities across the two-layers, which gives rise to a non-trivial overlapping structure particularly remarkable for some brain regions associated to given tasks; (c) we propose a novel method to identify core-periphery structures in networks with links of different types, and apply it to extract the multiplex core of our brain, highlighting previously neglected regions of interest. Results indicate the existence of a complex interplay between the structural and functional networks of the human brain, and that, even if structural links appears to be somehow necessary to determine the co-activation of two brain regions, functional connectivity is non-trivially constrained by its underlying anatomical network.

Abstract: One of the most important problems in biology is to understand the principles underlying evolution of living systems from non-living systems. To do so, we need to identify universal features of living systems that can distinguish them from other classes of physical systems. On the other hand, recently developed frameworks for control theory on complex networks suggest that our ultimate understanding of the evolutionary principles allow us to control biological networks in terms of making them to converge to desired states. Here, I present our recent attempt to understand relationship between informational architecture as the universal feature, and controllability by using various biomolecular networks. Our previous study showed that scaling relation of information processing within biological networks differentiates them from their random network counterparts. Also, we provided an analysis which indicates that biologically distinctive patterns of informational flow is related to control kernel, a minimal subset governing the global dynamics of biological networks. In this paper, we quantify controllability of a network by the size of control kernel, which is the size of the subset to be controlled to drive it from one state to the desired state. We find that biological networks tend to be more difficult to control compared to random networks. This suggests that biological networks are evolved to be more resilient to environmental change. In addition to that, we measure informational flow within biological networks with snd without control to investigate how the observed resilience is related to the informational processing. Finally, we discuss the implications of informational architecture and its relationship with controllability in understanding evolutionary principles for living systems.

Abstract: We consider classical nonlinear oscillators like rotators and Kuramoto oscillators on hexagonal lattices of small or intermediate size. When the coupling between these elements is repulsive and the bonds are frustrated, we observe coexisting states, each one with its own basin of attraction. For special lattices sizes the multiplicity of stationary states gets extremely rich. When disorder is introduced into the system by additive or multiplicative Gaussian noise, we observe a noise-driven migration of oscillator phases in a rather rough potential landscape. Upon this migration, a multitude of different escape times from one metastable state to the next is generated [1]. Based on these observations, it does not come as a surprise that the set of oscillators shows physical aging. Physical aging is characterized by non-exponential relaxation after a perturbation, breaking of time-translation invariance, and dynamical scaling. When our system of oscillators is quenched from the regime of a unique fixed point towards the regime of multistable limit-cycle solutions, the autocorrelation functions depend on the waiting time after the quench, so that time translation invariance is broken, and dynamical scaling is observed for a certain range of time scales [2]. We point to open questions concerning a possible relation between physical and biological aging. References: [1] F.Ionita, D.Labavic, M.Zaks, and H.Meyer-Ortmanns, Eur. Phys.J.B 86(12), 511 (2013). [2] F.Ionita, H.Meyer-Ortmanns, Phys.Rev.Lett.112, 094101 (2014).

Abstract: The origin and consequences of the nested structure of mutualistic ecosystems is a matter of strong debate in the ecological community. The relationship between the structure of mutualistic ecosystems and the dynamics that led to this structure is still an open problem. In the seminal paper of May[1], the ecosystem is described by a dynamical linear model, with a random matrix interaction. His results show that a large ecosystem with high connectivity is unstable. Since then, special attention has been paid to the structure of the interaction matrix. Bastolla et. al [2] study a population dynamics model that includes plant-animal mutualistic interactions and animal-animal and plant-plant competing interactions, in the mean field approach, except for the weak mutualism regime, where a more realistic mutualistic term is included. They conclude that the nestedness minimizes competition, allowing for an increase of biodiversity. A recent article [3] discusses the importance of structural stability of mutualistic ecosystems. In this work we investigate the influence of the network structure on the persistence of species of a mutualistic ecosystem. We study a non-linear population dynamics model where we take into account the structure of interactions both, in mutualistic and competition terms. In fact, the observed networks contain more information than just the plant-pollinator interactions. The ecosystem may be treated as a two layers of competing agents, one for plants and another for animals, coupled by the mutualistic interactions. This information can be then used to model the competition term beyond the mean-field approach. Our results show the existence of a trade-off between mutualism and competition, so that the largest biodiversity is achieved with a non-trivial combination of both mechanisms. [1] RM. May. Nature. 238, 413 (1972) [2] U. Bastolla et al. Nature. 458, 1018 (2009) [3] R.P. Rohr, S. Saavedra, J. Bascompte, Science 345, 416 (2014)

Abstract: The albatross was the first example of levy walk in animals, leading to the development of optimal foraging theories in levy walks. Other pelagic birds like the shearwaters presents a range of displacement distributed as a power law, but with an exponent different to the optimal foraging one, challenging the scientific community for a while. In this talk we show how the exponent of the power law in the pdf of displacement is simply the result of the law of first passage, related to the olfactory­cued navigation in shearwaters birds. Olfactory­cued navigation was proposed for a great variety of animals especially those one that navigate in featureless environment. We present the first mechanistic proof of olfactory­cued navigation showing the relation between the cut off of the pdf and the wind turbulence intensity.

Abstract: In this work we use belief-propagation techniques to study the equilibrium behaviour of a minimal model for the immune system comprising interacting T and B clones. We investigate the effect of the so-called idiotypic interactions among complementary B clones on the system's activation. Our result shows that B-B interactions increase the system's resilience to noise, making clonal activation more stable, while increasing the cross-talk between different clones. We derive analytically the noise level at which a B clone gets activated, in the absence of cross-talk, and find that this increases with the strength of idiotypic interactions and with the number of T cells signalling the B clone. We also derive, analytically and numerically, via population dynamics, the critical line where clonal cross-talk arises. Our approach allows us to derive the B clone size distribution, which can be experimentally measured and gives important information about the adaptive immune system response to antigens and vaccination.

Abstract: Healthy life is maintained through a complex regulating system in our bodies that ensure our dynamic functioning and keep vital physical and mental parameters within safe limits despite environmental challenges. Systemic resilience is the capacity of our complex systems to bounce back to normal functioning upon disturbances, ultimately determining our chances of survival and quality of life. As they age, humans gradually lose resilience which often remains unnoticed until confronted with a health crisis that is often detrimental to well-being and costly to society. We currently still lack valid methods to dynamically measure resilience for upcoming stressors. Emerging insights in other complex dynamical systems such as ecological networks, the climate and financial markets are uncovering generic empirical indicators that may be used to quantify systemic resilience dynamically: these early warning signals comprise changes in the dynamics of a system that are most clearly observed when the system recovers from a disturbance, which slows down upon decreasing resilience. Here we present integrative research in which we asked whether we can rank humans from resilient to frail by looking at differences in these dynamical characteristics in empirical data collected over time. We analysed time series of daily self-reported physical and mental health during 100 days in 22 elderly people ranging from frail to resilient as determined by a frailty index. The dynamics of the time series of a less resilient human system indeed turned out to be characterised by elevated variance and temporal autocorrelation. Additionally, as network theory predicts, as the different elements in a network of fluctuating elements lose resilience, deviations in the physical and mental domains of the system became more correlated. This contribution to the empirical evidence for the use of dynamical characteristics to quantify resilience across complex systems brings hope of foreseeing and preventing catastrophic failures in health.

Abstract: Protein-protein interactions are important in various areas of cell biology, including drug development for several diseases. Many therapeutic methods are based on complex algorithms supported by protein-protein interaction networks. Using a known mathematical model of the cell (from membrane computing), as well as a new abstract measure of complexity provided by proteins length, we study the computational power of protein-protein interaction systems involving a minimal number of cells/membranes with respect to the movement provided by endocytosis and exocytosis operations that are supported by proteins of different lengths. We proved that such protein-protein interaction networks can simulate all computable functions, and thus can be effectively used in designing efficient therapeutic algorithms for numerous diseases. We study the computational power of a pair of certain forms of endocytosis and exocytosis (namely pino and exo operations), and prove their universality by using at most three cells/membranes using proteins for both (pino) and (exo) operations of length at most two. We also study the computational power of the pair (phago) and (exo) operations, and prove their universality by using at most four cells/membranes, while the length of proteins of are at most two. The higher number of cells/membranes here is triggered by the use of the (phago) operation. These universality results means that the corresponding protein-protein interaction networks have the same computational power as a Turing machine, and so able to support all the complex algorithms (describing computable functions).

Abstract: The immune system is a fascinating complex system taking decisions on how to respond to a wide variety of stimuli, varying from lethal pathogens to harmless proteins in the food. This system relies on a large repertoire of randomly generated ‘detectors’ in the form of naïve lymphocytes. We are interested in the mechanisms behind the maintenance of these detectors during ageing. Nowadays, deep sequencing methods can be used to characterize the repertoires of naïve lymphocytes. To get a deeper understanding of how an important part of the complex immune system works, we developed a model regarding naïve lymphocyte dynamics which is similar to Hubbell’s Neutral Community Model used in ecology. The simulations and analytical solution of this model give a geometric clone-size distribution, whereas it has been argued that the repertoire is power-law distributed. In addition, we show that current diversity measures tend to overestimate the effect of ageing. Indeed, Next Generation Sequencing (NGS) data shows a geometric distribution, which is in line with our neutral model. Our simple model appears to be sufficient to describe the complex maintenance of naïve detectors in the immune system.

Abstract: The analysis of brain functional architecture is a paradigmatic example of complex system, since brain functionality emerges as a global property of local interactions. A complete description of multi-scale and multi-level segregation and integration of brain regions represents a challenging issue to address and unearths the complexity of its whole functional organization. Here we analysed functional magnetic resonance imaging data from forty human healthy subjects during resting condition. Network theory is able to visualize the skeleton of functional correlations (weights) between different regions (nodes) of the brain and to extract information, by selecting only the most important features out of the noise. On the resulting human functional brain network, we performed a modified version of the percolation analysis and compared the results with a null model: a not-trivial hierarchical organization in modules emerges. A zoom in the modular structure through a maximum spanning forest (MSF) approach unveiled a chain-like organization of the brain regions, never observed before. Intuitively, nodes tend to link with nodes in the same anatomical area, except for regions in the Temporal Lobe. Passing from the MSF to the maximum spanning tree, the network preserved the chain-like structure, confirming some outcomes and revealing the centrality of the Occipital Lobe and some regions from the Temporal Lobe and the Cerebellum. Furthermore, we explored the hierarchical organization of the brain function looking at network configurations when specific thresholds are introduced. Many connections within, rather than between, anatomical regions disclosed a high level of segregation of a specific area. Both the Occipital Lobe and the Cerebellum exhibit together this feature, even if an important difference emerged: while the former represents the core of the whole functional network with all the other modules connecting gradually to it, the latter is peripheral, joining the network only at the end.

Abstract: Influenza activity shows a complex spatio-temporal pattern with a strong seasonal dynamic whose complete understanding is still missing. Here we study 30 years of seasonal influenza circulation in France at the regional level from influenza-like-illness (ILI) cases time series. Our aim is to characterize common patterns of synchronization across seasons and assess how they change from the start of the outbreak to the time at which influenza activity reaches its peak. To each season we associate two vectors whose elements are the epidemic onset time or the epidemic peak time in a given region of France, normalized to remove seasonal trends. We cluster seasons according to their epidemic onset time (peak time) based on the distance between their corresponding vectors. We found that the distance computed on the peak time is generally smaller than the one computed on the onset time. A stronger clustering is therefore observed at the peak time, highlighting the larger synchronization that regions reach in the period of highest incidence with respect to the beginning of the epidemic. Seasons starting with rather different geographic distribution of epidemic onset become more similar in their peak time synchronization pattern. They are also characterized by larger epidemics and show no relevant correlation to weather time series, differently from the seasons not showing this recurrent pattern. Multi-scale transportation networks are found to play an important role in the emergence of such patterns. The study identifies the relevant factors in the shaping of the spatio-temporal diffusion of influenza in France, offering important information for the understanding of seasonal behavior and for the developments of realistic models of influenza spread.

Abstract: To better understand the complex relations between physical properties of foods and sensory perception, we have explored methods that are also used in the area of complex systems. We analysed temporal dominance of sensations (TDS) data [1] using methods from information theory[2]. We report that vastly different TDS curves can be mapped onto one master curve as a function of normalized time and an information theoretical measure. Furthermore the theory provides the basis for a recently proposed quantitative measure for complexity[3]. Interestingly, we find that this measure versus time maximizes at a point that is near to the moment of swallowing. This behaviour has a strong resemblance to that of a system near a phase transition[4]. Such an analogy has been put forward by Gershenson et al. for a random Boolean network[3]. New visualisation methods are investigated, like directed graphs, to research TDS profiles on an individual level for different food model systems. This showed that for most individuals the sequence of dominant sensations depended on system type, while for some it was independent of that. References [1] M. Devezeaux de Lavergne, M. van Delft, F. van de Velde, M. a. J. S. van Boekel, and M. Stieger, “Dynamic texture perception and oral processing of semi-solid food gels: Part 1: Comparison between QDA, progressive profiling and TDS,” Food Hydrocoll., vol. 43, pp. 207–217, 2015. [2] C. E. Shannon, “A Mathematical Theory of Communication,” Bell Syst. Tech. J., vol. 27, no. July, pp. 379–423, 1948. [3] C. Gershenson and N. Fernández, “Complexity and information: Measuring emergence, self-organization, and homeostasis at multiple scales,” Complexity, vol. 18, no. 2, pp. 29–44, Nov. 2012. [4] M. Prokopenko, J. T. Lizier, O. Obst, and X. R. Wang, “Relating Fisher information to order parameters,” Phys. Rev. E, vol. 84, no. 4, p. 041116, 2011.

Abstract: There is increasing evidence that aerosol transmission is a major contributor to the spread of influenza. Despite this, virtually all studies assessing the dynamics and control of influenza assume that it is transmitted solely through direct contact and large droplets that require close physical proximity. Here, we use wireless sensors to measure simultaneously both the location and close proximity contacts in the population of a US high school. This dataset, highly resolved in space and time, allows us to model both droplet and aerosol transmission either in isolation or in combination. In particular, it allows us to computationally assess the effectiveness of overlooked mitigation strategies such as improved ventilation that are available in the case of aerosol transmission. While the effects of the type of transmission on disease outbreak dynamics appear to be weak, we find that good ventilation could be as effective in mitigating outbreaks as vaccinating the majority of the population. In simulations using empirical transmission levels observed in Hong Kong and Bangkok households, we find that bringing ventilation to recommended levels has the same effect as vaccinating between 50% and 60% of the population, in the combined droplet-aerosol model. Our study therefore suggests that improvements of ventilation in public spaces could be an important strategy supplementing vaccination efforts for effective control of influenza spread.

Abstract: It is a long-standing question whether human sexual and reproductive cycles are affected predominantly by biology, hemisphere location, or culture. Here we show that interest in sex peaks sharply online during the major cultural and religious celebrations of countries with predominantly Christian or Muslim populations, regardless of hemisphere. These peaks in sex-related searches correspond to documented human birth cycles, even after adjusting sex-search data for numerous factors such as search language, season, amount of free time due to holidays, and changes in the overall volume of online searches. We further show that public mood sentiment measured independently from the Twitter content generated by the same country populations, contains distinct collective emotions associated with those cultural celebrations, even after removing all known greetings used during cultural and religious celebrations. Additionally, the observed collective moods correlate with sex search volume outside of these holidays. Our results provide converging evidence that the cyclic sexual and reproductive behavior of human populations is driven above all else by culture, and specifically that the seasonal sex-search and corresponding birth peaks derive from emotions that are maximized during major cultural and religious celebrations, but appear in other occasions when interest in sex also tends to increase.

Abstract: The transport of organelles and proteins is of vital importance for living cells. Besides passive transport by diffusion, active transport by molecular motors hopping over the cytoskeleton network is crucial for the survival of cells. We performed simulations using the Totally Asymmetric Exclusion Process (TASEP), a paradigmatic model for nonequilibrium transport, to model the dynamics along the complex microtubule network. We found that the rules at the intersection of the network seem to be the key factor for the formation of traffic jams along the microtubule segments. The rate at which motors at crossing continue along the same microtubule or switch to the other microtubule appears to determine for the transport along the network. Our simulations of the microtubule network reveal surprisingly rich behavior of the transport current with respect to the global density and exit rate ratio. We found four different regimes of motor propagation through network depending on the average motor density. For example, for low densities the current/density distribution through the network can be linearized and solved exactly. In contrast, for medium global densities the motor distribution through the network becomes highly inhomogeneous and non-linear leading to a huge reduction of the transport current through the system, when larger part of the network will be in ‘virtual’ traffic jam. We have also found a broad plateau of the current at the intermediate motor densities leading to stabilization of transport properties within such networks [1]. Due to the generality of the exclusion process in modeling transport and arrest phenomena, our results may provide generic insights into traffic jams and transport capacities of highway networks, biological networks, and other systems with similar unidirectional topology. [1] D. V. Denisov, D. M. Miedema, B. Nienhuis, and P. Schall, Phys. Rev. E 92, 052714 (2015).

Abstract: Competition for resources in biological context bears a resemblance to auction mechanisms, many agents compete but only a few (or only one) get the reward. But contrary to the well-studied auction models in economy, a reasonable assumption in this context is that everybody (not only the winner) pays their bid, e.g. time/energy invested to endure a conflict or foraging food. Following the work of Chatterjee et al. 2012, we look at the k-player all pay auctions searching for the states evolution might favour. We analyse these systems with an associated birth-death process governed by agent’s strategy success in the repeated interactions modeled as k-player all-pay auctions. In the large population limit, when the stochasticity can be neglected, we derive replicator equations whose fixed points are previously found Evolutionary Stable Strategies for these games. However, in previous works cycles were also noted that could not be explained at the level of deterministic description. We thus introduce back the stochasticity (the diffusion approximation) and the intrinsic noise, as we show, gives rise to the cyclic dynamics. We observe that the cycles are more present when the bidding strategy space is smaller, and when the number of participants in an auction (k) is small. As this description can be extended to the continuous strategy space, we find out that except for the 2-player auctions, cycles are property of games with discrete strategy space. Chatterjee, K., Reiter, J. G., Nowak, M. A., 2012. Evolutionary dynamics of biological auctions. Theoretical Population Biology, 81: 69-80.

Abstract: Neurodegenerative diseases, such as Alzheimer and Parkinson, are more common in western countries due to the longer expectation of life and the design of reliable tests for their early detection is becoming a pressing challenge. Several neurological disorders are associated with the aggregation of aberrant proteins, often localized in intracellular organelles such as the endoplasmic reticulum. We have studied protein aggregation kinetics and developed a model to follow the evolution of the aggregation and the critical role played by the cell endoplasmic reticulum. Moreover, since another important question is to be able to analyze protein aggregation in micron-scale samples but reproducible results are still hard to achieve, we have developed a strategy to quantify in silico the statistical errors associated to the detection of aggregation-prone samples. Alltogether, our work opens a new perspective on the understanding of these pathologies and on the forecasting of protein aggregation in asymptomatic subjects.

Abstract: The mesoscopic structure of complex brain networks is the key intermediate level of organisation bridging the microscopic dynamics of individual neurons with the macroscopic dynamics of the brain as a whole. At this mesoscopic level, brain activity tends to be organized in a modular way, with functionally related units being positively correlated with each other, while at the same time being relatively less (or even negatively) correlated with dissimilar ones. Such emergent organisation is mainly detected through the measurement of cross-correlations among time series of brain activity, the projection (usually via an arbitrary threshold) of these correlations to a network, and the subsequent search for denser modules (or so-called communities) in the network. It is well known that this approach suffers from an unavoidable information loss induced by the thresholding procedure. Another, less realized, limitation is the bias introduced by the use of network-based (as opposed to correlation-based) community detection methods. Here we discuss an improved method for the identification of functional brain modules based on random matrix theory. Our method is threshold-free, correlation-based, and very powerful in filtering out both local unit-specific noise and global system-wide dependencies. The approach is guaranteed to identify mesoscopic functional modules that, relative to the global signal, have an overall positive internal correlation and negative mutual correlation. We apply our method to time series of individual neurons in several samples of the suprachiasmatic nucleus (SCN) of mice, a small pacemaker region where strong spatial and temporal dependencies make the identification of substructure particularly challenging. We systematically detect two main functional modules, core and periphery, which are perfectly anti-correlated once the strong global signal is filtered out. These modules turn out to largely correspond to neuron populations with true biological differences, e.g. in the neurotransmitters used and in their coupling and synchronisation properties.

Abstract: The circadian clock in the suprachiasmatic nucleus (SCN), located in the hypothalamus in the brain, is important for the regulation of our daily and seasonal rhythms. It has been shown that the neuronal network organization of the SCN changes in different seasons, however, the mechanisms behind these changes are far from elusive. Furthermore, only a subset of neurons within the SCN network are directly responsive to light, which poses the question how encoding for seasons is achieved in the SCN network. Currently, not much is known about the function of the regional heterogeneity in the SCN in seasonal adaptation. Using time series of single cells we have applied a new community detection method to identify communities of cells in winter and summer conditions. This impartial method detected mostly two communities which we mapped to the SCN neuronal network and further characterized in their functional significance. Anterior regions encode for more phase dispersion, while posterior regions encode for more phase synchrony. Within the anterior SCN, the cells in the dorso-lateral region show more variability in their oscillatory periods in summer conditions, which means that these cells are more weakly coupled, enabling higher phase dispersion among the cells. Ventro-medially located cells in the anterior SCN and cells in the posterior SCN are more rigid in their oscillatory behaviour. This suggests that the cells in the dorso-lateral anterior region of the SCN play an active role in the phase adjustments of the SCN cells in different seasons. Our new network analysis approach enhances the identification and the subsequent functional characterization of neuronal clusters in the SCN, possibly paving the way for more elaborate network analysis on the level of single-cells in other brain regions.

Abstract: BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX3 can thus be used as a novel immersive platform for exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas.

Abstract: Type A influenza poses a serious risk to the health of the global population, due to its ability to inhabit a diverse range of hosts and having many strains. Very occasionally, humans become infected with a virus derived from non-human sources. These are essentially novel to humans. Due to the viruses meeting with little or no established resistance, they can, following mutation and adaptation to their new host, spread relatively easily in the human species. This can give rise to a localised outbreak that may develop into a worldwide influenza pandemic. Despite this, there is a worrying gap in the modelling of spillover transmission from animals to humans, a crucial element of the system in the lead up to an influenza pandemic event. We explore developments to address the lack of established mathematical modelling tools in this area, with the applied aim of being able to evaluate the effectiveness of control strategies in reducing pandemic risk. In particular, we overview two data-driven studies: (i) a statistical analysis of the time periods between influenza pandemics since 1700, to determine whether the emergence of new pandemic strains is either a memoryless or history-dependent process; (ii) constructing a spatial model, incorporating poultry-to-poultry and poultry-to-human transmission, applied to H5N1 epidemics in Bangladesh occurring between 2007 to 2011. These studies provide insights into the risk to humans associated with avian influenza outbreaks and the control strategies that should be utilised, across both human and livestock species, in the event of future influenza epidemics.

Abstract: A major cause of regime shifts in lake is nutrient overloads, coming mostly from agricultural fertilizers. Moreover, intensive agriculture has weakened soils which leads to a greater leach of nutrients during heavy rains. The objective of the paper is to model the effect of rainfall variability on lake regime shift, despite lake regulation. IPCC (Intergovernmental Panel on Climate Change) reports show that increases in extreme rain events are expected. Therefore, the multiplication of significant floods could result in nutrient over-enrichment, disrupting the equilibrium of lakes and causing eutrophication. Our case-study is the lake Bourget in France. We build and calibrate a model based on annual phosphorus dynamics. Results show that the drought of the 2000s is fostering a return to an oligotrophic state of the lake. We also show that lake regulation has been effective in reducing phosphorus input enabling the compliance of the objective recommended by OECD (Organisation for Economic Co-operation and Development) in 2020. A return to previous rainfall variability affects in a limited way the probability of a regime shift in the short and long terms. However, increasing the variance of loading by 25% may decrease from 98% to 81% the probability to maintain the lake Bourget in an oligotrophic state until 2100.

Abstract: Most disorders are caused by a combination of multiple genetic and environmental factors. If two diseases are caused by the same mechanism, they often co-occur in patients. Here we disentangle how much genetic or environmental risk factors contribute to the pathogenesis of 358 individual diseases, respectively. We pool data on genetic, pathway-based, and toxicogenomic disease-causing mechanisms with co-occurrences obtained from almost two million patients. From this data we construct a multilayer network where nodes represent disorders that are connected by links that either represent phenotypic comorbidity or the joint involvement of certain mechanisms. We quantify the similarity of phenotypic and mechanism-based links for each disorder. Most diseases are dominated by genetic risk factors, while environmental influences prevail for disorders such as depressions, cancers, or dermatitis. The relevance of environmental risk factors for a given disease is inversely related to its broad-sense heritability and also inversely related to the rate at which new drugs for the disease are approved. This might be indicative of a lack of successful drug development for diseases with high environmental risks. Our approach allows to rule out certain types of disease-causing mechanisms when their implied comorbidities are not observed and might therefore be used to identify promising leverage points for the development of future therapies of multifactorial diseases.

Abstract: Infectious agents affect behaviour and vital rates of their hosts, by influencing the interactions between species in the community and in that way are potentially changing the stability of the ecosystem. Empirical examples show a variety of ways in which different types of infectious agents can affect their hosts. We take an indirect approach in investigating wider community effects of these influences on hosts at different trophic levels. By decreasing and increasing resource preferences of consumers, conversion efficiencies and growth rates, we mimic subclinical and clinical influence of an infection in the community. Via the influence of infectious agents on their hosts, food webs become more and less stable, as it was measured by the size of the largest real part of the eigenvalues of the community matrix. The potential effects of the infectious agents show various consequences for the stability of the system even in the same focal species and role of that species as a consumer or resource. Our results show that influence of infection on resource preference of consumers has more impact on the change of stability than the effect of infection on conversion efficiencies of consumers. Subclinical and clinical effects of infectious agents in focal species of hosts, more frequently lead to increase than to decrease in stability of the community. The study suggests that infectious agents may be important for the stability of ecosystems.

Abstract: In self-organized ecosystems, it is eminent that group-level properties emerge from large-scale spatial pattern formation that promote survival of the organisms within the population. However, how these emergent properties influence the evolution of self-organizing traits and thereby affect spatial pattern formation itself remains unknown. Here, we demonstrate that aggregation into clusters in self-organized mussel beds adds a group-level selection pressure, which can cause the evolution of labyrinth-producing behaviour in mussels. We use a modelling approach that includes a high amount of ecological detail to investigate the evolution of two self-organizing traits, cooperation and aggregative movement, in spatially patterned mussel beds, where mussels aggregate and attach byssus threads (a glue-like substance) to neighbouring conspecifics in order to decrease losses to predation and wave stress. We developed a mechanistic, individual-based model of spatial self-organization where individual strategies of movement and attachment generate spatial patterns, which in turn determine the fitness consequences of these strategies. By combining an individual-based simulation approach for studying spatial self-organization within generations with an analytical adaptive dynamics approach that studies selection pressures across generations, we are able to predict how the evolutionary outcome is affected by environmental conditions. When selection pressures on cooperation and movement are only governed by local interactions, that is, the attachment of individuals to their neighbours, evolution does typically not result in the labyrinth-like spatial patterns that are characteristic for mussel beds. However, when we include a second level of selection by considering the additional protection provided by the formation of mussel clumps, evolutionarily stable movement and attachment strategies lead to labyrinth-like patterns under a wide range of conditions.

Abstract: Typical brain networks consist of many peripheral regions and a few highly central ones, i.e. hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown that networks, obtained from the analysis of specific frequency components of brain activity, present peculiar architectures with unique profiles of region centrality. However, the identification of hubs in networks built from different frequency bands simultaneously is still a challenging problem, remaining largely unexplored. Here we identify each frequency component with one layer of a multiplex network and face this challenge by exploiting the recent advances in the analysis of multiplex topologies. We first show that each frequency band carries unique topological information, fundamental to accurately model brain functional networks. This result suggests that information from frequency bands which are tipically neglected might play a crucial role in understanding brain's function. By using node's versatility, i.e. the natural extension of the concept of node's centrality in classical networks to multilayer sistems, we then demonstrate that hubs in the multiplex network are in general different from those ones obtained after discarding or aggregating the measured signals as usual and provide a more accurate map of brain's most important functional regions. Finally, as a clinical application, we use the brain's versatility profile to distinguish between healthy and schizophrenic populations, achieving higher accuracy than conventional network approaches.

Abstract: Rapidly evaluating novel control measures against infectious diseases can be challenging, especially when the disease has a low incidence and it is characterized by a high degree of complexity. This is the case of invasive meningococcal diseases (IMD), rare but severe diseases hitting primarily infants, caused by bacteria asymptomatically carried by more than 10% of the general population, mostly among adolescents. A novel meningococcal vaccine, Bexsero, has been recently included for the first time in a national immunization programme, in the UK. This represents an unprecedented chance to evaluate such a vaccine. However, traditional statistical studies require large samples of observed disease cases to provide precise estimations. Thus, it may require several years of surveillance to precisely assess the effectiveness of Bexsero. We used a Monte Carlo Maximum Likelihood (MCML) approach to estimate both direct and indirect effectiveness of meningococcal vaccines. The method is based on stochastic simulations of an age-structured SIS model reproducing meningococcal transmission and vaccination. We calibrated the model to describe two immunization campaigns in the UK: the Bexsero campaign, started in the last fall, and a previous campaign, started in the 1999 and employing a different meningococcal vaccine, whose effectiveness has been already assessed using traditional studies. MCML estimates of vaccine effectiveness for the 1999 campaign are in good agreement with estimates from traditional studies, yet characterized by smaller confidence intervals. Also, we show that the MCML method could provide a fast and accurate estimate of the effectiveness of Bexsero, with a time gain that ranges from 2 to 15 years, depending on the value of effectiveness measured from field data. Our results show that inference methods based on dynamic computational models can be successfully used to quantify in near real-time the effectiveness of immunization campaigns, providing an important tool to complement and support traditional studies.

Abstract: Recent work has indicated that tropical forest and savanna can be alternative stable states under a range of climatic conditions. However, based on dynamical systems theory it may be expected that in case of strong spatial interactions between patches of alternative stable states, their coexistence becomes unstable. Boundaries between forest and savanna would then only be stable at conditions where the two states have equal potential, called the ‘Maxwell point’. Under different conditions, the state with the lowest potential would not be resilient against invasion of the state with highest potential. We used frequency distributions of MODIS tree-cover data at 250 m resolution to estimate Maxwell points with respect to the amount and seasonality of rainfall in both South America and Africa. We tested on a 0.5° scale whether there is a larger probability of local coexistence of forests and savannas near the estimated Maxwell points. Maxwell points for South America and Africa were estimated at 1760 mm and 1580 mm mean annual precipitation and at Markham’s Seasonality Index values of 50% and 24%. Although the probability of local coexistence was indeed highest around these Maxwell points, local coexistence was not limited to the Maxwell points. We conclude that critical transitions between forest and savanna may occur when climatic changes exceed a critical value. However, we also conclude that spatial interactions between patches of forest and savanna may reduce the hysteresis that can be observed in isolated patches, causing more predictable forest-savanna boundaries than continental-scale analyses of tree cover indicate. This effect could be less pronounced in Africa than in South America, where the forest-savanna boundary is substantially affected by rainfall seasonality.

Abstract: The main disabling factor of epilepsy is the sudden and usually unpredictable occurrence of seizures. However, seizures are not uniformly distributed in time. Periods of increased and decreased probability of seizure occurrence were observed in patients and in chronic models of epilepsy. Complex systems approaches helped to uncover power-law behaviour in distributions of seizure energy and inter-seizure intervals (ISI). The increase of the conditional waiting time until the next event with increasing the waiting time for the preceding event indicates a memory in the seizure dynamics. We have examined long-term seizure dynamics in the tetanus toxin model of temporal lobe epilepsy in eight adult rats. In all animals periods of high seizure frequency (clusters) were interspersed with periods of seizure absence or low seizure frequency. Concatenated data from all clusters confirm scale-free behaviour with the characteristic conditional waiting time behaviour. The study of individual clusters shows that seizures have a specific time-dependent dynamics. The clusters start with randomly occurring weaker seizures separated by short ISI’s which are followed by a progressive increase of ISI’s and seizure severity. In the present study we have concentrated on synchronization and information transfer in electrocorticographic signals in order to characterize the connectivity of epileptic networks in different parts of clusters, i.e. in seizures of different severity. An information-theoretic approach for detecting information transfer within and across different time scales, already successfully applied in a different multiscale complex system (M. Palus, Phys. Rev. Lett. 112(7), 078702, 2014) has been adapted for analysis of electrocorticograms. Understanding the mechanisms of the transition to seizures, and initiation and termination of seizure clusters can open new ways for the development of techniques for seizure forecasting and prevention. Support by the Czech Science Foundation (GACR 14-02634S) is gratefully acknowledged.

Abstract: The original form of the replicator equation was the first important tool to connect game dynamics, where individuals change their strategy over time, with evolutionary game theory, created by Maynard Smith and Price to predict the prevalence of competing strategies in evolving populations. The replicator equation was initially developed for infinitely large and well-mixed populations. Later, in 2006, using the standard pair approximation, H. Ohtsuki and M. Nowak proved that moving evolutionary game dynamics from a well-mixed population (a complete graph) onto a regular non-complete graph is simply described by a transformation of the payoff matrix. Under the assumption of weak selection, and using a new closure method for the pair approximation technique, we build a modified replicator equation on infinitely large graphs, for birth-death updating (a player is chosen with probability proportional to its fitness, and the offspring of this player replaces a random neighbour). The closure method that we propose takes into account the probability of triangles in the graph. Using this new equation, we study how graph structure can affect cooperation in some games with two different strategies, namely the Prisoner's Dilemma, the Snow-Drift Game and the Coordination Game. We compare our results with the ones that were obtained in the past using the standard replicator equation and the Ohtsuki-Nowak replicator equation on graphs. We also discuss how our modified pair approximation performs on different graphs, when compared to other approaches, and how it can be generalized, still satisfying the consistency conditions.

Abstract: The evolution is modeled by three main forces: genetic drift, mutation and selection. The most of complexity of biological life that arises from these simple operations is a result of their interplay. Not only different time scales characteristic to these mechanisms are responsible for the observed genetic variety, but also the diploid organization of the organisms that use sexual reproduction. This latter is a base for three kinds of selection: directional, underdominance and overdominance. Directional selection does not take advantage of diploid organization: its effect in diploid organisms is similar to that observed in haploid forms of life. Underdominance is a mechanism responsible for unstable allele frequency equilibrium and as such it is rarely observed in the nature. Some scientists consider it as a significant force leading to speciation. The third type, overdominance, is responsible for balancing selection because it results from stable allele frequency equilibrium. How strongly this genetic force may change genetic composition as compared with the neutral Kimura’s model shaped mostly by the genetic drift, is presented by the results of simulations using the author’s software. The outcomes of simulations are further compared with the predictions of the overdominance equilibrium model. The mechanism leading to observed interspecies polymorphism (for example the human-chimpanzee polymorphism detected in ATM gene by author’s earlier works) is explained based on results of simulated evolution in neutral and balancing selection models. Finally, the overwhelming complexity of contemporary life is considered in the light of serious bottlenecks for complexity present at the early life, such as complexity threshold and the limitation in the number of different genes before chromosomal organization of genome occurred. Significance of chromosomes as genetic information carriers further duplicated in diploid cells is concluded as a major architectural advantage required for the observed complexity of the life.

Abstract: In order to correctly divide, cells have to move all their chromosomes at the center, a process known as congression. This task is performed by the combined action of molecular motors and randomly growing and shrinking microtubules. Chromosomes are captured by growing microtubules and transported by motors using the same microtubules as tracks (1). Coherent motion occurs as a result of a large collection of random and deterministic dynamical events. Understanding this process is important since a failure in chromosome segregation can lead to chromosomal instability one of the hallmarks of cancer. We describe this complex process in a three dimensional computational model involving thousands of microtubules. The results show that coherent and robust chromosome congression can only happen if the total number of microtubules is neither too small, nor too large. Our results allow for a coherent interpretation a variety of biological factors already associated in the past with chromosomal instability and related pathological conditions (2). (1) Z. Bertalan et al. Navigation Strategies of Motor Proteins on Decorated Tracks PLoS One 10 e0136945 (2015) (2) Z. Bertalan et al. Role of the Number of Microtubules in Chromosome Segregation during Cell Divison, PLoS One, 10 e0141305 (2015).

Abstract: The phenomenon of “human aging” is a matter which has implications for society, the economy and policymaking. The global proportion of elderly people (defined as those above the age of 60), standing at 11.7% in 2013, is projected to reach 21.1% by 2050. Multiple nations worldwide facing aging populations also face concomitant economic and social pressures. Facing the challenges associated with an aging population requires a holistic approach to human aging, as it spans multiple domains (such as physical, metabolic, immunological, cognitive, psychological and social aspects). Here we report a network-theoretic procedure to characterize the inter-associations among the biomarkers obtained from Singapore Longitudinal Aging Study (SLAS)-2 elderly cohort (N = 3270), as well as evaluate strategies to obtain a small subset of representative biomarkers from the larger biomarker set of 1581 variables. From a network built from calculations of statistically-significant pairwise effect sizes between biomarker variables, we obtain a minimum spanning tree consisting of 1373 variables from the network’s giant cluster (the rest being singletons), and apply the Louvain maximum-modularity community-detection algorithm on the tree which are composed of biomarkers which are highly-associated with each other. We examine the thematic similarities to group the clusters into higher-order thematic groups. We also compare the performance of various machine learning models in predicting SAGE, a multi-modal index of successful or unsuccessful aging, as opposed to using the entire complement of biomarkers. The procedures proposed in this work simultaneously considers both numerical and categorical biomarkers (something not done previously to our knowledge). Furthermore, the results we obtained here are important in both the characterization of a group of elderly people, establishing a hierarchy of importance among their biomarkers, as well as obtaining candidate subsets of biomarkers for measurement and evaluation, something which requires less time and resources compared to obtaining the full set.

Abstract: We consider the model of Boolean genetic regulatory networks named GPBN established in [1]. A GPBN is a directed graph with two different classes of nodes; G and P, representing genes and proteins respectively. For every node we consider only states 0 or 1 (0 means inactive, 1 active). Each gene is strictly linked with a unique specific protein P, but a set of different proteins may influence the activation (or inactivation) of a given gene. The novelty of this model consists that each active protein will remain active throughout a fixed delay of time steps. In the classic Boolean network the delays are one for every node. Given a GPBN with N nodes and a set of delays (dti≥1; i=1,..., N) we prove that its dynamics is equivalent to a usual Boolean network (without delays) with N + SUM (dti) nodes. Furthermore, for the class of disjunctive Boolean networks (i.e., at each node the local activation is an OR function) we prove, by using the previous equivalence, that any GPBN admits only fixed points in spite of the fact that when this class of networks is updated like the usual ones (delays equal to one) they may have limit cycles with super-polynomial periods. Finally, we illustrate the behavior of GPBN by studying the dynamics of one-dimensional elementary cellular automata. Roughly, we observe, from exhaustive simulations, that the majority of the 256 elementary automata converge to fixed point or to confined limit cycles, from that we may conclude that the information transmission in automata with delays is unusual. [1] A. Graudenzi, R. Serra, M. Villani, C. Damiani, A. Colacci, and S. Kauffman. Dynamical properties of a boolean model of gene regulatory network with memory. Journal of Computational Biology, 18:1291–1303, 2011.

Abstract: The human brain is composed of anatomically defined regions characterized by diverse histological, structural and functional connectivity profiles [1]. Previous work showed that genes that are consistently highly expressed across subjects show correlations to both brain structure and function, strongly suggesting a crucial role of differential transcription in modulating the genetic expression patterns across different regions, thus producing canonical gene-specific signatures for brain modules[2]. In this contribution, we study the whole genetic expression signatures of all regions across six subjects from the Allen Human Brain Atlas[1]. We produce an individual topological network of genes co-expression, akin to a coarse-grained backbone, via an extension of the topological simplification algorithm Mapper[3]. This new topological backbone is obtained by slicing the whole sample space, obtaining local clusters and then glueing them together according to a set-overlapping rule. This transformation solves the analysis problems caused by the combined properties of high-dimensionality, due to the large number of genes studied (~30k), and the relative sparsity of the samples (a few hundreds per subject). The resulting backbone preserves the shape of the original dataset while strongly reducing its dimensionality and yields a notion of network connectivity across the gene expression samples. We find that samples from known anatomical modules localize coherently on the backbone occupying almost non overlapping subnetworks formed by compact connected components. This reveals both the spatial architecture of gene (co)expression,as well as the interactions between the different modules. These subnetworks can provide maps to understand the interactions between the genetic pathways of neurotransmitters, an all important step in understanding the complex chemical interactions in the brain. For example, how a pharmaceutical interventions,that target a specific subsystem, such as anti-psychotic targeting the dopamine system, will impact the other sub-systems. 1.Hawrylycz,M.J. et al. Nature 489, 391–399(2013). 2.Hawrylycz,M.J. et al. 18, 1832–1844(2015). 3.Singh,G.,Mémoli,F. & Carlsson,G.E. SPBG(2007).